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Large Language Models for Code: Exploring the Landscape, Opportunities, and Challenges
In the rapidly evolving landscape of software development, Large Language Models (LLMs) for code have emerged as a groundbreaking tool for code completion, synthesis and analysis. In this talk, we will explore the current developments of these models, understand how they are trained, and how they can be leveraged with custom codebases. Additionally, we will address potential risks and challenges, including privacy concerns, using insights from projects like the BigCode initiative and StarCoder models.
Speaker
Loubna Ben Allal
Machine Learning Engineer @Hugging Face
Loubna Ben Allal is a Machine Learning Engineer in the Science team at Hugging Face working on Large Language Models for code & synthetic data generation. She is part of the core team behind the BigCode Project and has co-authored The Stack dataset and StarCoder models for code generation. Loubna holds Mathematics & Deep Learning Master's Degrees from Ecole des Mines de Nancy and ENS Paris Saclay.
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